Quality assurance in clinical pathology plays a critical role in the management of patients with prostate cancer, as pathology is the gold standard of diagnosis and forms a cornerstone of patient therapy. Methods to integrate quality development, quality maintenance, and quality improvement to ensure accurate and consistent test results are, hence, critical to cancer management. These factors have a direct bearing on patient outcomes, financial aspects of disease management as well as malpractice concerns. One of the major failings in prostate pathology today is the rate of missed tumors and variability in grading. It is well known that the grading of prostate tissues suffers from intra-and inter-pathologist variability [1]. In the studies of intra-and inter-pathologist reproducibility [2, 3], the exact intra-pathologist agreement was achieved in 43-78% of the instances, and in 36-81% of the instances, the exact inter-pathologist agreement was reported. It is also known that the variability of the grading could be reduced after pathologists are re-trained. There could be many ways to educate pathologists such as meetings, courses, online tutorials, etc. [4], but these are not time-and cost-effective for routine everyday decisions. Therefore, building an automated, fast, and objective method to aid pathologists to examine prostate tissues will greatly help to attain reliable and consistent diagnoses.
Several automated systems for the grading of prostate tissues have been developed [5-14]. The majority of systems use texture and/or morphological features to characterize and classify tissue samples into correct classes. However, the information which pathologists obtain by using such methods is limited since these only provide the predicted grade in general. The prediction also relies on the training data. These prior efforts always sought to match a sample completely to provide a diagnosis, rather than provide matching candidates. Further, the role of other modalities in the process was not clear.